Primary exploratory analysis
Filter cells according to their quality metrics
| Parameter | Min threshold | Max threshold |
|---|---|---|
| nFeature_RNA | 400 | +Inf |
| nCount_RNA | -Inf | 20000 |
| Percent.mito | -Inf | 0.1 |
| Dropouts | -Inf | 0.97 |
Before 1051 cells –> After 1026 cells
Identification of highly variable genes (HVGs) and dimension reduction (PCA)
Median UMI per cell : 1587.5
Nb highly variable genes : 2000
## PC_ 1
## Positive: VIM, SPARCL1, AQP1, CAV1, ID3, GNG11, ID1, DARC, TSPAN7, IGFBP4
## VWF, PLVAP, CLEC14A, KRT15, RAMP2, ECSCR, EMCN, CD34, ENG, A2M
## CLDN5, ESAM, TMEM88, COX7A1, HSPG2, CCL14, ELTD1, PRCP, NRN1, TIMP3
## Negative: C1orf194, C9orf24, ATPIF1, FAM183A, C9orf116, RSPH1, AC013264.2, SNTN, DYNLL1, C5orf49
## CETN2, C20orf85, PIFO, CAPS, SMIM22, MORN2, AGR3, TPPP3, DYNLRB2, B9D1
## ODF3B, AKAP14, TUBB4B, MORN5, FAM229B, C12orf75, NME5, CRIP1, LRRC46, RP11-128M1.1
## PC_ 2
## Positive: SPARCL1, IGFBP7, VIM, AQP1, GNG11, SNTN, CAV1, ID3, DARC, VWF
## C1orf194, AC013264.2, RSPH1, TNFAIP8L1, TSPAN7, C20orf85, STOML3, FAM92B, MORN5, RP11-128M1.1
## ODF3B, TUBA1A, PPP1R42, C2orf40, CCDC146, PROS1, PLVAP, CD34, ENG, C9orf24
## Negative: MGST1, CXCL1, MDK, CXCL17, VMO1, WFDC2, KRT19, FAM3D, CTSC, ELF3
## TSPAN8, SCGB1A1, AGR2, KRT7, SLPI, RHOV, CYP2F1, TACSTD2, CD55, XBP1
## IL8, CTD-2531D15.4, CP, CLDN7, LCN2, KRT18, CXCL6, CLCA2, EPCAM, KLK11
## PC_ 3
## Positive: KRT15, S100A2, SOD3, TAGLN, ACTA2, TPM2, MYL9, TPM1, DSTN, MT1X
## KRT19, PDLIM3, CNN1, PPP1R14A, ACTG2, DES, TACSTD2, MYH11, RP5-977B1.11, MYLK
## RARRES2, MFAP4, AEBP1, PLN, CKB, CSRP1, BCHE, FXYD1, PCSK7, ASPN
## Negative: CD74, TMSB4X, HLA-DRA, FCER1G, HLA-DPB1, AIF1, TYROBP, HLA-DPA1, HLA-DRB1, HLA-DMA
## HLA-DQB1, HLA-DQA2, CST3, ITGB2, MS4A6A, TMSB10, HLA-DRB5, MNDA, HLA-DQA1, CORO1A
## CPVL, TBXAS1, ARHGDIB, HLA-DQB2, HLA-DMB, HCST, COTL1, TYMP, AOAH, LY86
## PC_ 4
## Positive: TAGLN, ACTA2, MYL9, TPM2, PPP1R14A, SOD3, ACTB, CNN1, ACTG2, TPM1
## PDLIM3, LGALS1, DSTN, MYLK, DES, PCSK7, MYH11, AEBP1, BCHE, MFAP4
## RP5-977B1.11, PLN, CSRP1, FHL1, FLNA, ASPN, PDLIM7, COX7A1, FAM83D, CALD1
## Negative: ID1, GNG11, DARC, AQP1, TSPAN7, IFI27, ID3, VWF, PLVAP, RAMP2
## KRT15, EMCN, ECSCR, EGFL7, CCL14, NRN1, CLDN5, ENG, S100A2, ELTD1
## TMEM88, KLF2, CLEC14A, JAM2, ESAM, PRCP, ITM2A, TM4SF1, NOSTRIN, RAMP3
## PC_ 5
## Positive: KRT15, S100A2, FCER1G, AIF1, TYROBP, MT1X, RNASET2, ITGB2, MS4A6A, HCST
## MNDA, CORO1A, LAPTM5, PTPRC, TBXAS1, PRDX5, CSF1R, LST1, LY86, ALDH3A1
## ALOX5AP, AOAH, CD68, LGALS2, RNASE6, EMP3, FGL2, KRT19, CTSS, EVI2B
## Negative: SPARCL1, VIM, IGFBP7, GNG11, AQP1, ID3, ID1, DARC, IFI27, TSPAN7
## PLVAP, VWF, ECSCR, EMCN, ENG, EGFL7, RAMP2, CLDN5, CCL14, ELTD1
## CD34, PRCP, NRN1, CAV1, KLF2, HYAL2, ESAM, JAM2, CLEC14A, TM4SF1
Nb PCs to use : PCs : 1:10
Dataset embbedings (t-SNE and UMAP) and clustering (Louvain algorithm)
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 1026
## Number of edges: 13572
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9102
## Number of communities: 11
## Elapsed time: 0 seconds
Distribution of the quality metrics per cluster
Identification of cluster marker genes (Wilcoxon’s rank test)
Cell type labelling
##
## Basal Suprabasal Endothelial Smooth muscle Macrophage
## 364 180 233 89 50
## Secretory Fibroblast Serous Multiciliated
## 33 32 25 20
Recompute top marker genes for each cell population 
## R version 3.5.2 (2018-12-20)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Debian GNU/Linux 10 (buster)
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.3.5.so
##
## locale:
## [1] LC_CTYPE=fr_FR.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=fr_FR.UTF-8 LC_COLLATE=fr_FR.UTF-8
## [5] LC_MONETARY=fr_FR.UTF-8 LC_MESSAGES=fr_FR.UTF-8
## [7] LC_PAPER=fr_FR.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggrepel_0.8.0 cowplot_0.9.4 ggplot2_3.1.0 dplyr_0.8.3
## [5] tidyr_0.8.2 Seurat_3.0.0.9000
##
## loaded via a namespace (and not attached):
## [1] httr_1.4.0 viridisLite_0.3.0 jsonlite_1.6
## [4] splines_3.5.2 lsei_1.2-0 R.utils_2.8.0
## [7] gtools_3.8.1 Rdpack_0.10-1 assertthat_0.2.1
## [10] yaml_2.2.0 globals_0.12.4 pillar_1.3.1
## [13] lattice_0.20-38 reticulate_1.11.1 glue_1.3.0
## [16] digest_0.6.20 RColorBrewer_1.1-2 SDMTools_1.1-221
## [19] colorspace_1.4-0 htmltools_0.3.6 Matrix_1.2-16
## [22] R.oo_1.22.0 plyr_1.8.4 pkgconfig_2.0.2
## [25] bibtex_0.4.2 tsne_0.1-3 listenv_0.7.0
## [28] purrr_0.3.3 scales_1.0.0 RANN_2.6.1
## [31] gdata_2.18.0 Rtsne_0.15 tibble_2.0.1
## [34] withr_2.1.2 ROCR_1.0-7 pbapply_1.4-0
## [37] lazyeval_0.2.1 survival_2.43-3 magrittr_1.5
## [40] crayon_1.3.4 evaluate_0.13 R.methodsS3_1.7.1
## [43] future_1.12.0 nlme_3.1-137 MASS_7.3-51.1
## [46] gplots_3.0.1.1 ica_1.0-2 tools_3.5.2
## [49] fitdistrplus_1.0-14 data.table_1.12.2 gbRd_0.4-11
## [52] stringr_1.4.0 plotly_4.8.0 munsell_0.5.0
## [55] cluster_2.0.7-1 irlba_2.3.3 compiler_3.5.2
## [58] rsvd_1.0.0 caTools_1.17.1.1 rlang_0.4.0
## [61] grid_3.5.2 ggridges_0.5.1 htmlwidgets_1.3
## [64] igraph_1.2.4 labeling_0.3 bitops_1.0-6
## [67] rmarkdown_1.11 npsurv_0.4-0 gtable_0.2.0
## [70] codetools_0.2-16 R6_2.4.0 zoo_1.8-5
## [73] knitr_1.21 future.apply_1.2.0 KernSmooth_2.23-15
## [76] metap_1.1 ape_5.3 stringi_1.2.4
## [79] parallel_3.5.2 Rcpp_1.0.3 png_0.1-7
## [82] tidyselect_0.2.5 xfun_0.4 lmtest_0.9-36